Add check for ctc_loss targets param (#150981)

Fixes #150835

## Test Result

```python
# cuda
>>> import torch
>>> import torch.nn.functional as F
>>> device = "cuda" # "cpu" is fine
>>> num_classes = 4
>>> log_probs = torch.rand(0, 0, num_classes, device=device)
>>> targets = torch.tensor([], device=device, dtype=torch.long)
>>> input_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> target_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> result = F.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/zong/code/pytorch/torch/nn/functional.py", line 3079, in ctc_loss
    return torch.ctc_loss(
           ^^^^^^^^^^^^^^^
RuntimeError: log_probs tensor must not be empty

# cpu
>>> device = "cpu"
>>> num_classes = 4
>>> log_probs = torch.rand(0, 0, num_classes, device=device)
>>> targets = torch.tensor([], device=device, dtype=torch.long)
>>> input_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> target_lengths = torch.tensor([], device=device, dtype=torch.long)
>>> result = F.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/zong/code/pytorch/torch/nn/functional.py", line 3079, in ctc_loss
    return torch.ctc_loss(
           ^^^^^^^^^^^^^^^
RuntimeError: log_probs tensor must not be empty

```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150981
Approved by: https://github.com/eqy

aten/src/ATen/native/LossCTC.cpp

@@ -126,6 +126,7 @@ std::tuple<Tensor, Tensor, size_t, std::vector<int64_t>> ctc_loss_allocate_outpu
 // the alphas from the user by only returning the loss.
 template<typename scalar_t, ScalarType target_scalar_type>
 std::tuple<Tensor, Tensor> ctc_loss_cpu_template(const Tensor& log_probs, const Tensor& targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t BLANK) {
+  TORCH_CHECK(log_probs.numel() > 0, "log_probs tensor must not be empty");
   // log_probs: input_len x batch_size x num_labels
   // targets [int64]: batch_size x target_length OR sum(target_lengths)
   constexpr scalar_t neginf = -std::numeric_limits<scalar_t>::infinity();

aten/src/ATen/native/cuda/LossCTC.cu

@@ -219,6 +219,7 @@ ctc_loss_log_alpha_gpu_kernel(scalar_t* __restrict__ log_alpha_data,
 // backward. The dispatch function will only return the loss.
 template<typename scalar_t, ScalarType target_scalar_type>
 std::tuple<Tensor, Tensor> ctc_loss_gpu_template(const Tensor& log_probs, const Tensor& targets, IntArrayRef input_lengths, IntArrayRef target_lengths, int64_t BLANK) {
+  TORCH_CHECK(log_probs.numel() > 0, "log_probs tensor must not be empty");
   // log_probs: input_len x batch_size x num_labels
   // targets [int64]: batch_size x target_length OR sum(target_lengths)
   CheckedFrom c = "ctc_loss_gpu";

test/test_nn.py

@@ -11532,6 +11532,15 @@ class TestNNDeviceType(NNTestCase):
         grad_cudnn, = torch.autograd.grad(loss_cudnn, log_probs, grad_out)
         self.assertEqual(grad_cudnn, grad_native, atol=1e-4, rtol=0)
 
+    @expectedFailureMPS
+    def test_ctc_loss_error(self, device):
+        log_probs = torch.rand(0, 0, 4, device=device)
+        targets = torch.tensor([], device=device, dtype=torch.long)
+        input_lengths = torch.tensor([], device=device, dtype=torch.long)
+        target_lengths = torch.tensor([], device=device, dtype=torch.long)
+        with self.assertRaisesRegex(RuntimeError, "log_probs tensor must not be empty"):
+            F.ctc_loss(log_probs, targets, input_lengths, target_lengths, reduction='none')
+
     @expectedFailureMPS  # RuntimeError: LSTM with projections is not currently supported with MPS.
     @dtypesIfCUDA(torch.half, torch.float, torch.double)
     @dtypes(torch.float)